Variable-Order Fractional Diffusion Model-Based Medical Image Denoising | Zendy

A. Abirami | Zendy; P. Prakash | Zendy; YongKi Ma | Zendy

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Variable-Order Fractional Diffusion Model-Based Medical Image Denoising

Author(s) -

A. Abirami,

P. Prakash,

YongKi Ma

Publication year - 2021

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2021/8050017

Subject(s) - noise reduction , variable (mathematics) , diffusion , noise (video) , stability (learning theory) , mathematics , fractional calculus , integer (computer science) , order (exchange) , mathematical optimization , image (mathematics) , computer science , mathematical analysis , artificial intelligence , physics , machine learning , finance , economics , thermodynamics , programming language

Fractional differential models are playing a vital role in many applications such as diffusion, probability potential theory, and scattering theory. In this study, the variable-order space and time fractional diffusion model is employed for denoising the medical images. The finite difference approach is implemented to find the numerical solution of the proposed model. Convergence and stability of the numerical method are presented. The experimental outcomes of the variable-order model are analyzed with those of the fractional and integer-order diffusion models. It was noticed that the peak signal-to-noise ratio (PSNR) value is increased considerably for the proposed model.

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